Publications

Selected Papers

# for co-first author
[35] Ma, D., Cheng, S., Luo, Z., Deng, Y., Du, M., Shu, L., ... & Wang, L.* (2026). Study on real-time prediction for porosity in laser melting based on a digital twin-driven deep learning framework. Mechanical Systems and Signal Processing, 255, 114453. [link]
[34] Guo, W., Yang, L., Xiao, Q., Li, E., Liu, H., Li, Y., ... & Wang, L.* (2026). Enhancing sound absorption in graded gyroid structures: The role of axial gradients and additive manufacturing. Ocean Eng, 361, 126166. [link]
[33] Peng, G., Guo, Z., Du, M., Wang, G., Wang, L., & Yan, W.* (2026). Ductilizing Additively Manufactured Paramagnetic Alloys via Auxiliary Magnetic Field: Beyond the Porosity Effect. International Journal of Machine Tools and Manufacture, 104422. [link]
[32] Ju, Y., Wang, L., Hu, D., Yu, Y., & Yan, W.* (2026). The mechanism of grain refinement of AlSi10Mg in laser additive manufacturing under static magnetic field. npj Computational Materials. [link]
[31] Yu, M., Zhu, L., Yang, Z., Ning, J., Xue, P., Wang, S., Wang, L., & Tan, X. (2026). Machine learning-assisted process-structure-property correlation in laser metal additive manufacturing: a critical review. International Journal of Extreme Manufacturing, 8(4), 042005. [link]
[30] Zhang, D., Guo, Z., Li, Y., Wang, L., Wu, Y., Sun, D., ... & Zhang, B. (2026). Deformation-Induced Formation of Stray Grains in Additive Manufacturing of Single Crystals. Advanced Science, 13(25), e22704. [link]
[29] Deng, Q., Wang, L., Chen, F., Li, E., Liu, Z., Wang, W., ... & Yan, W. (2026). Unveiling cold cracking and grain refinement mechanisms governed by alloy composition in additive manufacturing of high-strength Mg-Gd (-Zn)-Zr alloys. Journal of Magnesium and Alloys, 102090. [link]
[28] Huang, Z., Hu, D., Wang, L., Wang, L., & Fu, M. (2026). Eliminating anisotropy and unlocking ultra-high strength in TA15 via in-situ nitrogen microalloying during micro laser powder bed fusion. Progress in Additive Manufacturing, 1-19. [link]
[27] Su, J., Jiang, F., Wu, J., Li, Y., Han, R., Wang, L., ... & Sing, S. L. (2026). Machine learning driven discovery of low modulus biomedical titanium alloys for additive manufacturing. Nature Communications. [link]
[26] Zhou, Q., Guo, R., Liu, S., Li, N., Xu, M., Zhang, X., Jiang, X., Wang, L., ... & Hou, Y. (2026). Additive-assisted perovskite crystallization on industrial TOPCon silicon for tandem solar cells with improved efficiency. Nature Energy, 1-11. [link]
[25] Wang, L.*, Guo, Z., Peng, G., Wu, S., Zhang, W., & Yan, W.* (2025). Evaporation-Induced Composition Evolution in Metal Additive Manufacturing. Advanced Functional Materials, 35(7), 2412071. [link]
[24] Sun, X., Wang, L., Peng, G., Wang, G., Lu, Y., Sakane, S., ... & Fu, M. W.*. (2025). A fully coupled multi-physics multi-phase field crystal plasticity finite element model (MPF-CPFEM) for predicting microstructure evolution and thermomechanical behavior in additive manufacturing. International Journal of Plasticity, 104583. [link]
[23] Wu J., Zhang C., Wang G., Cao H., Zheng P., Liu W., ...,Wang, L.*, Yan, W.*. (2025). Physics-assisted feature-augmented metamodels to predict the keyhole geometry in laser additive manufacturing. Additive Manufacturing, 104916. [link]
[22] Wang G., Zhang Y., Liu J., Chen W., Wang K., Cui B., Zou B., Ouyang Q., Zhang Y.,Wang, L., ... & Ma E. (2025). Dispersion hardening using amorphous nanoparticles deployed via additive manufacturing. Nature Communications, 16(1), 3589. [link]
[21] Zhang Y., Wu S., Guo Z., Peng G.,Wang, L., & Yan, W. (2025). Defects caused by powder spattering and entrainment in laser powder bed fusion process: High-fidelity modeling of gas, melt pool and powder dynamics. Acta Materialia, 288, 120816. [link]
[20] Liu Y., Hu Z., Wang, L., Zhai Z., Yan W., & Li, Z. (2025). A new mechanism for laser absorption in high-reflectivity metal powder beds modified with ceramic particles. Journal of Applied Physics, 137(13). [link]
[19] Li R., Ma H., Wang R., Song H., Zhou X., Wang, L., .. & Xia, C.. (2025). Application of unsupervised learning methods based on video data for real-time anomaly detection in wire arc additive manufacturing. Journal of Manufacturing Processes, 143, 37-55. [link]
[18] Chia, H. Y., Zhang, Y., Wang, L., & Yan, W. (2024). Unveiling gas–liquid metal reactions in metal additive manufacturing: High-fidelity modeling validated with experiments. Acta Materialia, 275, 120029. [link]
[17] Deng, Q., Chen, F., Wang, L., Liu, Z., Wu, Q., Chang, Z., ... & Ding, W. (2024). Exceptional strength paired with increased cold cracking susceptibility in laser powder bed fusion of a Mg-RE alloy. Journal of Materials Science & Technology. [link]
[16] Xie, Z., Chen, F., Wang, L., Ge, W., & Yan, W. (2024). Data-driven prediction of keyhole features in metal additive manufacturing based on physics-based simulation. Journal of Intelligent Manufacturing, 35(5), 2313-2326. [link]
[15] Wang, L., Guo, Q., Chen, L., & Yan, W. (2023). In-situ experimental and high-fidelity modelling tools to advance understanding of metal additive manufacturing. International Journal of Machine Tools and Manufacture, 104077. [link]
[14] Wang, L., Wang, S., Zhang, Y., & Yan, W. (2023). Multi-phase flow simulation of powder streaming in laser-based directed energy deposition. International Journal of Heat and Mass Transfer, 212, 124240. [link]
[13] Wang, Y.#, Wang, L.#, Liu, D., Miao, B., Wu, H., Pei, J., ... & Yuan, G. (2023). Mechanisms of processing map difference between laser powder bed fusion of Mg solid cubes and lattice structures. Additive Manufacturing, 76, 103773. [link]
[12] Chia, H. Y., Wang, L., & Yan, W. (2023). Influence of oxygen content on melt pool dynamics in metal additive manufacturing: High-fidelity modeling with experimental validation. Acta Materialia, 249, 118824. [link]
[11] Du, D., Wang, L., Dong, A., Yan, W., Zhu, G., & Sun, B. (2022). Promoting the densification and grain refinement with assistance of static magnetic field in laser powder bed fusion. International Journal of Machine Tools and Manufacture, 183, 103965. [link]
[10] Zhang, Y., Yu, Y., Wang, L., Li, Y., Lin, F., & Yan, W. (2022). Dispersion of reinforcing micro-particles in the powder bed fusion additive manufacturing of metal matrix composites. Acta Materialia, 235, 118086. [link]
[9] Yu, Y., Wang, L., Zhou, J., Li, H., Li, Y., Yan, W., & Lin, F. (2022). Impact of fluid flow on the dendrite growth and the formation of new grains in additive manufacturing. Additive Manufacturing, 55, 102832. [link]
[8] Zhang, S.#, Ding, M.#, Wang, L.#, Ge, W., & Yan, W. (2022). Laser powder bed fusion of diamond/N6 MMCs enabled by Ni-Ti coated diamond particles. Materials & Design, 217, 110635. [link]
[7] Hu, D., Grilli, N., Wang, L., Yang, M., & Yan, W. (2022). Microscale residual stresses in additively manufactured stainless steel: Computational simulation. Journal of the Mechanics and Physics of Solids, 161, 104822. [link]
[6] Wang, L., Zhang, Y., Chia, H. Y., & Yan, W. (2022). Mechanism of keyhole pore formation in metal additive manufacturing. npj Computational Materials, 8(1), 22. [link]
[5] Yang, M., Wang, L., & Yan, W. (2021). Phase-field modeling of grain evolution in additive manufacturing with addition of reinforcing particles. Additive Manufacturing, 47, 102286. [link]
[4] Wang, L., & Yan, W. (2021). Thermoelectric magnetohydrodynamic model for laser-based metal additive manufacturing. Physical Review Applied, 15(6), 064051. [link]
[3] Yang, M., Wang, L., & Yan, W. (2021). Phase-field modeling of grain evolutions in additive manufacturing from nucleation, growth, to coarsening. Npj Computational Materials, 7(1), 56. [link]
[2] Han, Y.#, Wang, L.#, Liu, K., & Yan, W. (2020). Numerical modeling of laser powder bed fusion of metallic glasses: Prediction of crystallization. Journal of Micromechanics and Molecular Physics, 5(04), 2050013. [link]
[1] Wang, L., Zhang, Y., & Yan, W. (2020). Evaporation model for keyhole dynamics during additive manufacturing of metal. Physical Review Applied, 14(6), 064039. [link]

Chapters

[1] Wang, L., Yefeng Yu, Daijun Hu, Wentao Yan, “Chapter 9: Multiscale modeling applied to additive manufacturing”, Fundamentals of Multiscale Modeling of Structural Materials, W. Xia, Ed., 1st Edition, Elsevier, 2022, pp. 333–388.